A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

TL;DR

This paper introduces Fuzzy Optimum-Path Forest, an enhanced classifier that integrates fuzzy logic to learn sample memberships unsupervised, improving classification robustness across diverse datasets.

Contribution

The paper presents a novel Fuzzy OPF framework that incorporates unsupervised membership learning to enhance the standard OPF classifier's performance.

Findings

Robust performance across twelve public datasets

Comparable to standard OPF in worst-case scenarios

Improved sample relevance identification

Abstract

In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for both supervised, semi-supervised, and unsupervised learning named Optimum-Path Forest (OPF) was proposed with competitive results in several applications, besides comprising a low computational burden. In this paper, we propose the Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier that learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over twelve public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst-case scenarios.